Social Network Based Recommendation Method and System

ABSTRACT

Recommendations for content may be generated based on social networking communities. For example, a user may receive a list of recommended content items based on content that has been viewed by others in the user&#39;s social networks. Recommendations may further be based on content information such as reviews, ratings, tags, attributes and the like from various sources internal and external to the user&#39;s social networks. Content items may be given a weight that corresponds to a determined level of relevance or interest to a user. Using the weight, a list of recommended items may be sorted or filtered. In one or more configurations, the weight may be modified based on an age of the content item. For example, the relevance, importance or interest of a news report may decline as the news becomes older and older.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of and claims the benefitof priority from U.S. application Ser. No. 12/437,682, entitled “SOCIALNETWORK BASED RECOMMENDATION METHOD AND SYSTEM,” and filed on May 8,2009. The content of the aforementioned application is incorporatedherein by reference in its entirety.

TECHNICAL FIELD

Aspects of the disclosure relate to providing content recommendations.More specifically, aspects of the disclosure are directed to providingcontent recommendations based on social networks.

BACKGROUND

With the amount of media available today, it can be difficult for aperson to identify content items that are of interest, importance orrelevance to him or her. Oftentimes, a person may spend significantamounts of time browsing through content items to manually identify anews article, movie, song or other content item that interests him orher. Further, the individual might only identify recommended orpreferred items based on personal preferences and already-knowninformation about the content items without the benefit of externalinfluences such as the person's social networks.

BRIEF SUMMARY

The following presents a simplified summary of the disclosure in orderto provide a basic understanding of some aspects. It is not intended toidentify key or critical elements of the disclosure or to delineate thescope of the disclosure. The following summary merely presents someconcepts of the disclosure in a simplified form as a prelude to the moredetailed description provided below.

One or more aspects described herein relate to generating content itemrecommendations based on social network information. For example,content item recommendations for a user may be determined based oncontent items that members of the user's social networks have accessed,tagged, viewed, commented on or otherwise interacted with. Content itemrecommendations may further be determined from other content informationsources such as internal and external content information databases(e.g., IMDB.COM), user preferences, advertisements and the like.

According to another aspect, content items may be ranked in arecommendations list based on the interactions and attributes of eachitem. Thus, comments about a content item stored in a social networkingcommunity may be used as a basis for increasing or decreasing therelevance, potential interest or importance of the content item.Similarly, attributes such as genre, title, description,actors/actresses and the like may be used as further considerations inranking and weighting content items.

According to yet another aspect, content item weights may be modifiedover time. In one embodiment, a content item's weight may decay overtime due to the age of the content item. This may represent a decreasein relevance, importance or interest as a content item becomes older.Decay rates may vary depending on the type of content item. For example,news articles may decay at a faster rate than blockbuster movies.

Other embodiments can be partially or wholly implemented on acomputer-readable medium, for example, by storing computer-executableinstructions or modules, or by utilizing computer-readable datastructures.

The methods and systems of the above-referenced embodiments may alsoinclude other additional elements, steps, computer-executableinstructions, or computer-readable data structures. In this regard,other embodiments are disclosed and claimed herein as well.

The details of these and other embodiments are set forth in theaccompanying drawings and the description below. Other features andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 illustrates an example network distribution system in whichcontent items may be provided to subscribing clients.

FIG. 2 illustrates an example block diagram of an apparatus forreceiving content item data and generating content item recommendationsaccording to one or more aspects described herein.

FIG. 3 illustrates an example method for generating content itemrecommendations based on social network information according to one ormore aspects described herein.

FIG. 4 illustrates an example data flow diagram for generating contentitem recommendations according to one or more aspects described herein.

FIG. 5 illustrates an example method for sorting and filtering a list ofrecommended content items according to one or more aspects describedherein.

FIG. 6 illustrates an example method for modifying content item weightsand/or ranks according to one or more aspects described herein.

FIG. 7 illustrates an example modifier schedule according to one or moreaspects described herein.

FIG. 8 illustrates an example interface for displaying and interactingwith a recommendations list according to one or more aspects describedherein.

FIG. 9 illustrates an example interface for setting interest strengthaccording to one or more aspects described herein.

DETAILED DESCRIPTION

FIG. 1 illustrates a content processing and distribution system 100. Thedistribution system 100 may include a headend 102, a network 104, settop boxes (STB) 106 and corresponding receiving and/or transmittingdevices (i.e., receiver, transceiver, etc.) 108. The distribution system100 may be used as a media service provider/subscriber system whereinthe provider (or vendor) generally operates the headend 102 and thenetwork 104 and also provides a subscriber (i.e., client, customer,service purchaser, user, etc.) with the STB 106.

The STB 106 is generally located at the subscriber location such as asubscriber's home, a tavern, a hotel room, a business, etc., and thereceiving device 108 is generally provided by the subscribing client.The receiving device 108 may include a television, high definitiontelevision (HDTV), monitor, host viewing device, MP3 player, audioreceiver, radio, communication device, personal computer, media player,digital video recorder, game playing device, cable modem, etc. Thedevice 108 may be implemented as a transceiver having interactivecapability in connection with the STB 106, the headend 102 or both theSTB 106 and the headend 102.

The headend 102 is generally electrically coupled to the network 104,the network 104 is generally electrically coupled to the STB 106, andeach STB 106 is generally electrically coupled to the respective device108. The electrical coupling may be implemented as any appropriatehard-wired (e.g., twisted pair, untwisted conductors, coaxial cable,fiber optic cable, hybrid fiber cable, etc.) or wireless (e.g., radiofrequency, microwave, infrared, etc.) coupling and protocol (e.g., HomePlug, HomePNA, IEEE 802.11(a-b), Bluetooth, HomeRF, etc.) to meet thedesign criteria of a particular application. While the distributionsystem 100 is illustrated showing one STB 106 coupled to one respectivereceiving device 108, each STB 106 may be configured with having thecapability of coupling more than one device 108.

The headend 102 may include a plurality of devices 110 (e.g., devices110 a-110 n) such as data servers, computers, processors, securityencryption and decryption apparatuses or systems, and the likeconfigured to provide video and audio data (e.g., movies, music,television programming, games, and the like), processing equipment(e.g., provider operated subscriber account processing servers),television service transceivers (e.g., transceivers for standardbroadcast television and radio, digital television, HDTV, audio, MP3,text messaging, gaming, etc.), and the like. At least one of the devices110 (e.g., a sender security device 110 x), may include a securitysystem.

The network 104 is generally configured as a media stream distributionnetwork (e.g., cable, satellite, and the like) that is configured toselectively distribute (i.e., transmit and receive) media serviceprovider streams (e.g., standard broadcast television and radio, digitaltelevision, HDTV, VOD, audio, MP3, text messaging, games, etc.) forexample, to the STBs 106 and to the receivers 108. The stream isgenerally distributed based upon (or in response to) subscriberinformation. For example, the level of service the client has purchased(e.g., basic service, premium movie channels, etc.), the type of servicethe client has requested (e.g., standard TV, HDTV, interactivemessaging, etc.), and the like may determine the media streams that aresent to (and received from) a particular subscriber.

In one or more embodiments, network 104 may further provide access to awide area network (WAN) 112 such as the Internet. Accordingly, STB 106or headend 102 may have access to content and data on the wide areanetwork. In one example, a service provider may allow a subscriber toaccess websites 114 and content providers 116 connected to the Internet(i.e., WAN 112) using the STB 106. Websites 114 may include news sites,social networking sites, personal webpages and the like. In anotherexample, a service provider (e.g., a media provider) may supplement orcustomize media data sent to a subscriber's STB 106 using data from theWAN 112. As further described herein, content recommendations may begenerated by a service provider (e.g., headend 102) using contentinformation from a WAN such as the Internet.

FIG. 2 illustrates a block diagram of a recommendation system 201 thatmay be used to process content item information and generate a list ofrecommended content items for a user. Content items, as used herein,refers generally to any type of media including visual, audio and print.For example, content items may include videos such as television shows,movies, interviews, news shows, audio including sound bits, music,books-on-tape and print such as news reports, articles, books,magazines, and the like. According to one or more embodiments,recommendation system 201 may include a processor 203 configured toexecute instructions and carry out mathematical calculations, memorysuch as random access memory (RAM) 205, read only memory (ROM) 207 anddatabase 209. Database 209 may be configured to store a variety ofinformation such as application data, user preference information,content items, application programming and the like. Recommendationsystem 201 may further include a communication interface 211 configuredto provide the recommendation system 201 with receiving and transmissioncapabilities. Recommendation system 201 may, for example, receive dataabout content items from other recommendation systems or rating enginessuch as IMDB.COM and ROTTENTOMATOES.COM. Alternatively or additionally,recommendation system 201 may receive content item data from othersources such as social networking communities where members may accessand otherwise interact with content items. Recommendation system 201 maysimilarly transmit content item data and recommendations (e.g., to asubscriber) using communication interface 211.

Content may be gathered from diverse sources by an aggregation module213 that may be configured to poll or be pinged by a variety of sourcesfor content item data and generate an aggregated list of content itemsas described in further detail herein. Content relevance and importancemay be calculated by activity module 221. For example, activity modulemay be configured to calculate a weight based on different actions onthat content by members of the user's social networks as well as theglobal population. Sorting module 215, on the other hand, may beconfigured to sort the recommended content items based on a relevance orinterest weight. Further, a decay module 217 may also operate inconjunction with aggregation module 213 and activity module 221 tomodify the weights of content items based on the age of a content item.Sorting and decaying of content item relevance or interest are alsodescribed in further detail herein.

Recommendation system 201 may be, in one or more arrangements, a part ofa service provider, e.g., as a portion of headend 102 (FIG. 1).Alternatively, recommendation system 201 may be separate from headend102 and may be implemented in one or more computers associated orlocated within with set-top boxes. In yet another embodiment,recommendation system 201 may be a distributed module that includesoperations both within headend 102 and external to headend 102. Thecomponents of recommendation system 201 may be configured as hardware,software, firmware and/or combinations thereof.

In one or more configurations, recommendation system 201 may furtherinclude a tracker module 219 that is configured to track userinteractions with content when presented to a user and interpret thoseinteractions as indicating interest or non-interest in various contentitems. Such interactions may be interpreted by tracker module 219 tocollect further data that may be relevant to providing recommendationsto other users who may be part of the user's social network. Forexample, tracker module 219 may detect a subscriber's interaction with apay-per-view movie. Based on the interaction, the tracker module 219 maydetermine that the subscriber has a strong interest in the pay-per-viewmovie. Additionally or alternatively, tracker module 219 may determinethat the subscriber is interested in a genre of movie with which thepay-per-view content is associated. Tracker module 219 may have theflexibility to interpret multiple types of interactions in a variety ofways.

FIG. 3 illustrates a method for making content recommendations for auser based on activity and information in the user's social network. Instep 300, a recommendation system may receive or otherwise determineuser preference information. User preference information may correspondto topics of interest, content genres (e.g., comedy, science fiction,action/adventure), time periods of interest (e.g., the 70s or 60s),preferred actors/actresses and the like. User preference information maybe determined explicitly from user input or implicitly based on userinteractions and behavior. In one example, user preference informationmay be received from a user in the form of keywords. In another example,user preference information may be determined based on tracking theactors or actresses the user most frequently watches. Users may furtherspecify a strength of interest in a keyword, actor, genre and the like.Various other preference determination mechanisms may be used.

In one or more configurations, the user may also be presented a visualaid to allow them to enter relative importance of their interestscompared to each other. FIG. 9 illustrates an example user interfacewhere a user may specify strengths of interest. Interface 900 includes alist of interests 903, each having a corresponding slider bar 905.Slider bar 905 may represent a range of interest strength, e.g., fromweak interest to strong interest. Alternatively, the range of slider bar905 may be represented by a numeric scale such as 1 to 10. Option 910may also be provided in interface 900 to allow a user to add additionalinterests.

Referring again to FIG. 3, in step 305, a user's membership in one ormore social network communities may be determined. For example, therecommendation system may request that a user identify the socialnetwork communities of which he or she is a part. Additionally, therecommendation system may request the user's authorization informationso that the recommendation system may access the user's social networkcommunities and retrieve content item information therefrom. In someconfigurations, a user's membership in social network communities may beidentified through analyzing cookies stored by a network browser, anetwork browsing history, data stored on the user's computing deviceand/or combinations thereof. However, confirmation and authorizationinformation (e.g., login and password) for accessing information in thesocial network community might still be requested.

In step 310, the system may identify one or more content items thatmatch the determined user preferences from a content database.Furthermore content found on the user's extended network not in thedatabase can be added to the database. For example, the system mayperform a keyword search to identify matches in genre, actor/actressnames, words in titles or descriptions of the content or words used inthe content itself (e.g., words spoken or used) and the like. Contentitems may also be identified from the content database based on one ormore other criteria including user demographics (e.g., age, gender,ethnicity), geographic location, overall content popularity and thelike. In step 315, the identified content items may be added to a listof potential content recommendations.

In step 320, the system may build on a potential recommendations list bydetermining content items that have been viewed or otherwise accessed bymembers of the user's social networks in the social network community.The social network site may return information such as the names oridentifiers of accessed content items, a number of times accessed,comments associated with the content item, a number of positive ornegative votes received by the content item, an average user rating andthe like. Accessing a content item may include rating the item,commenting on the content, tagging the content item, sending a link tothe content item, sending the content item itself and the like. Socialnetwork sites may track and log content access and interaction of theirusers and make such information available to other applications orservices with proper authorization. The recommendation system may suchcontent access information through data access or retrieval protocolsprovided by the social networking site or community.

In one or more arrangements, a social networking site might only provideor otherwise make accessible the content access data for members of auser's social networks. A social network or group, as used herein,refers generally to a social structure of nodes (e.g., individuals ororganizations) that are tied by one or more types of interdependencysuch as values, ideas, friendship, kinship and the like. Accordingly,the content access information of members of a social network may bevaluable in generating recommendations for a user that is also a memberof that social network. A social network community, on the other hand,generally refers to a service or system that facilitates the creation,maintenance and management of social networks. For example, FACEBOOK isa social network community that allows users to create social networkssuch as interest groups therein. In one example, a social network maycomprise a group of the user's friends. In another example, a socialnetwork may comprise a group of users sharing a similar interest intravel. In one or more arrangements, a social network site may alsoindicate the particular social network from which a particular piece ofcontent access information is derived (e.g., FACEBOOK may indicate thata Iron Man trailer was viewed 25 times by members of a Marvel Comicssocial network).

Alternatively or additionally, accessing of content items by members ofa social network may be tracked by the recommendation system for thoseindividuals that also use or subscribe to the recommendation system.Accordingly, the recommendation system may search within its owndatabases to obtain content access data of such individuals in a user'ssocial network. In step 325, the content items identified through thesocial network communities may be added to the list of potential contentrecommendations. Optionally, in one or more configurations, the list ofpotential content recommendations may be presented to the user asrecommended content items.

FIG. 4 illustrates a data flow of recommendation information and databetween a recommendation system 401 (e.g., system 201 of FIG. 2), asocial networking site 403, other external content information sources404 and a user device 405. As illustrated, recommendation system 401 mayobtain potential content recommendations from each of a system database407 and social networking site or communities 403 for a user. A socialnetworking site or community, e.g., site 403 a, may include multipledifferent social networks or groups 404. Each network or group may haverestricted or open membership; i.e., some groups may requireauthorization for memberships while other groups may allow anyone tojoin. For example, a user may control who is in his or her friends orinterest group by requiring approval to be added to the group.

To obtain content access information for a social network from socialnetworking sites 403, recommendation system 401 may transmitvalidation/authorization information (e.g., the user's credentials) toone or more of sites 403 depending on whether the user is a memberthereof In response, one or more of social networking sites 403 may sendback the requested data including information identifying contentaccessed by other members of the social networks of which the user isalso a member. Social networking sites 403 may each include a database411 that is configured to track and store content access information ofits users. Such information may then be made available to recommendationsystems such as system 401 through, in one example, an applicationprotocol interface so long as proper authorization information isprovided (e.g., login and password of a member user). In one or moreconfigurations, only content access information for members of theuser's social networks might be made accessible to the recommendationsystem.

Recommendation system 401 may further use information from externalcontent information sources 404 to enhance or otherwise modify therecommendation list (e.g., taking into account reviews orcategorizations of content items). Once the content data has beenprocessed and the list of content recommendations generated, the listmay be transmitted to user device 405. In one or more configurations,the recommendation system 401 may be associated with a filtering/sortingmodule (e.g., sorting module 215 of FIG. 2) configured to pare down orsort the recommendation list according to various parameters andspecifications. In one embodiment, the recommendation system 401 may beconfigured to provide video content recommendations to a user via userdevice 405. User device 405 may, for example, include a set-top box (notshown) configured to receive programming information, recommendationsand content from recommendation system 401 and/or other content servers(not shown).

Because the list of potential content recommendations may includes tens,hundreds or thousands of results, the list may be filtered or ordered sothat the most relevant content items are presented to the user first.For example, if a list of recommendations includes 250 results, the listmay be filtered down to the 10, 25, 50 or 100 most relevant to bepresented to the user. If the user wishes, he or she may view the next10, 25, 50 or 100 most relevant and so on. Filtering and ordering mayalso be used regardless of list size. Filtering and sorting may beperformed by taking into account a variety of parameters and criteriaincluding the level of user preference matching, a social circle weightand a system recommendation weight. The level of user preferencematching generally refers to how well a content item matches the userpreferences, demographics or geographic information.

For example, the level of match may be based on a number of keywordsmatched in the content description, metadata, title and the like. Socialcircle weight generally refers to a recommendation modifier determinedbased on activity in a user's social network involving the content item.As noted, such activity may include tagging, commenting on, sending,and/or rating the content item. System recommendation weight refersgenerally to a recommendation modifier that takes into accountattributes and activity surrounding a content item external to socialnetwork activity and user preferences. For example, systemrecommendation weight may take into account reviews of the content by anewspaper, a system designation of whether the content is featured ornormal, tagging, rating or commenting of the content by others outsidethe user's social network and the like. Based on these weights andconsiderations, an overall weight or rank for each content item in alist of potential recommendations may be ordered or filtered. Tagging,comments, ratings and other interactions with a content item may bedetermined, in one or more examples, by examining metadata associatedwith a content item.

FIG. 5 illustrates a method for sorting and filtering a list of contentitems according to one or more aspects described herein. In step 500, anaggregation module may determine or receive a list of content items. Thelist of content items may be, for example, a list of potentialrecommendations generated as described in FIG. 3. In step 505, themodule may determine whether the list includes a content item that isnot included in a recommendations database. If so, the content item maybe added to the database so that the content item may be tracked by therecommendation system in step 510. Additionally, the module maydetermine a social network weight, a user preference weight and a systemrecommendation weight for the content item in step 515. In one example,a user preference weight may be calculated by determining a number ofpreferences matched and adding together each of the preferences'respective interest weight. Thus, if a video is about Obama and Iraq anda user is interested in Obama with an interest level of 10 and Iraq withan interest level of 2, the user preference weight may equal 12 (i.e.,10+2). Social network weight, on the other hand, may be calculated basedon a number of votes received from members of a social network, anaverage rating, number of positive or negative comments and the like.System recommendation weight is computed by calculating a correlationcoefficient of the video to videos the user has rated highly as well asa correlation coefficient computed against videos rated highly by thelarger population of all the users using the recommendation system. Inone example, a total user preference weight may be calculated using theformula:

Total User Preference Weight=E(keyword*keyword-weight),

where keyword corresponds to a number of occurrences of the keyword in acontent item and keyword-weight corresponds to an associated weight ofthat keyword (e.g., user assigned). Accordingly, the total userpreference weight may correspond to a summation of keywords occurring ina content item multiplied by its keyword-weight. Social circle weightmay be calculated using the following example formula:

Social Circle Weight=E(normalized-friend-weight*interaction-weight)

where the normalized-friend-weight corresponds to a weight associatedwith a friend and normalized across different social networks and socialnetwork sites while an interaction-weight corresponds to the weight of aparticular interactions. For example, the act of rating a content itemwith 1 star may have a weight of 1 and that of 5 stars may be 5. Inanother example, a comment on the video may have a weight of 3. Eachinteraction made by individuals in the social circle weight may besummed to determine a total social circle weight.

System recommendation weight may be calculated by determining acorrelation coefficient between a content item and a user's favoritecontent items and adding the coefficient to a second correlationcoefficient between the content item and all content items rated highlyby other users. In one arrangement, the correlation coefficients maycomprise Pearson product-moment correlation coefficients calculatedbased on similarity of content item type, subject matter, actors, lengthand/or other attributes or combination of attributes. Furtherdescription of Pearson product-moment correlation coefficients may befound athttp://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient.

In step 520, a total content item weight may be generated based on thesocial network weight, the user preference weight and the systemrecommendation weight. For example, the total content item weight may becalculated by summing the social network weight, user preference weightand system recommendation weight. Alternatively or additionally, one ormore of the component weights may be modified by a level of importance.For instance, if user preference is considered to be a more significantrecommendation criterion, the user preference weight may be multipliedby a factor of 1.5. In another example, the multiplicative product ofthe component weights may equal the total content item weight. Variousother algorithms or formulas may be used to generate a total contentitem weight based on one or more of the component weights. The totalcontent item weight may be recorded in step 525.

Once the total content item weight has been generated and recorded, theprocess may revert back to step 505 where the module may determinewhether the list includes any content items for which a total contentitem weight has not been determined. If so, steps 510 through 525 arerepeated. If a total content item weight has been generated for allcontent items in the list, however, the process may proceed to step 530where the items in the list are ordered according to the determinedtotal content item weight. For example, if a larger total content itemweight corresponds to a stronger relevance or recommendation, the listmay be sorted from largest to smallest total content item weight toprovide a list presenting the recommended content items from mostrelevant or recommended to least relevant or recommended. In one or morearrangements, if a threshold has been set for a number ofrecommendations to return or a minimum content item weight, the list maybe filtered based on the threshold in step 535. In particular, if theuser or a default specifies that only 20 results are to be returned,only the top 20 results may be presented to the user as arecommendations list. If a minimum content item weight has beenspecified, on the other hand, those items that do not meet the minimumweight might not be presented to the user. The user may, however, begiven the option to view those results that were filtered out by one ormore filters.

For some categories of content, relevance and importance may be directlytied to a recency of the content. For example, news article about anevent may be considered most relevant or important the minuteimmediately after the occurrence of the event. As time passes, however,the news article (and the event) may become less relevant or importantas the news' significance or impact may have lessened. Accordingly, alist of recommended content items may adjust its content item rankingbased on a recency factor to account for such temporal bias.

According to one or more embodiments, a content item's total weight maybe adjusted by a recency modifier.

FIG. 6 illustrates a method for modifying a content item weight tocompensate for a recency factor. In step 600, for example, a weightingmodule (e.g., part of aggregation module 213 or recommendation system201 of FIG. 2) may identify a type of content associated with a contentitem. The type of the content may correspond to various categories suchas television shows, news content, commercials, time dependent content,time independent content and the like. In step 605, the weighting modulemay determine whether the type of content is subject to recency ortemporal dependency. For example, news content may be considered to havestrong temporal dependency while a commercial or a television show mightnot. In such instances, news content items may be identified by theweighting module in step 605. Alternatively, all types of content may besubject to recency or temporal dependency and thus, the determination ofstep 605 might not be needed.

In step 610, the weighting module may determine a recency modifier toapply to the weight or rank of the content item. In one example, therecency modifier may be determined from a modifier table that associatesmodifier values with a recency value. Alternatively or additionally, arecency modifier may be generated through a formula or algorithm. Forinstance, if a news report is less than 1 day old, the recency modifiermay be 1.0 while 2-day old news reports may correspond to a recencymodifier of 0.95. If the news report is 2 weeks old, however, theassociated recency modifier may be 0.70. According to one aspect, adecay rate may be specified in addition or as an alternative to arecency modifier schedule. Decay rate, as used herein, refers generallyto the speed or pace at which a content item's weight or rank is reducedas a function of time or age. In one or more configurations, and asdescribed in further detail herein, a recency modifier schedule or decayrate may be specific to a content item, type of content item, a user, acontent source and/or combinations thereof In step 615, the recencymodifier may be applied to the content item weight by taking themultiplicative product of the content item weight and the recencymodifier. Thus, a final weight for a content item may be calculated bythe formula:

Final weight=recency modifier*content-item-weight

Alternatively, the final weight may be calculated using the decay rateusing the formula:

Final weight=total−content-item-weight*e ^(kt),

where e corresponds to the mathematical constant e, k corresponds to thedecay rate and t corresponds to the age of the content item. Once thefinal weight for each content item has been calculated, a list ofcontent items may be re-ordered taking into account the modified contentitem weight in step 620.

According to one aspect, while the decay factor or recency modifier mayreduce a final weight of a video or other content item, the total weightmay increase based on user interaction on the video. For example, themore interaction a user or social network users have with a video, thehigher the social circle weight, system recommendation weight and totalweight will be. Accordingly, the speed with which a video decays out ofa user's recommended list might not be solely controlled by decay rate,but may also be affected by user interactions.

FIG. 7 illustrates a recency modifier schedule associating multiple ageranges of content items to corresponding recency modifiers. Inparticular, schedule 700 specifies ranges 703 of less than 2 days old,less than 1 week old, less than 2 weeks old, less than 1 month old, lessthan 3 months old, less than 6 months old, less than 1 year old and 1year or older. For each range, a recency modifier 705 is assigned.Accordingly, if a content item falls into one of the ranges, e.g., lessthan 2 weeks old, the total content weight of the content item may beadjusted by the corresponding recency modifier, e.g., 0.65. Asillustrated, any content item that is 1 year old or older may be givenno weight at all by using a recency modifier of 0. Additionally, weightsof any content item that is less than 2 days old might not be adjusted,signifying that content items less than 2 days old are still consideredfully relevant. The modifier schedule may be stored as a lookup table sothat a weight modifier module may retrieve a corresponding modifiervalue based on the age of a content item.

In some embodiments, the decay rate or weight modifier may be expressedin terms of a half-life. That is, the weight modifier may be expressedas an amount of time that is required for the weight of a content itemto decay or decrease by 50%. Alternatively or additionally, the weightmodifier or decay rate may be specified as a linear or non-linearformula that converts a content item age to a modifier value. Accordingto another aspect, decay rates or weight modifiers may be personalized.Stated differently, the speed at which a content item or type of contentitem loses weight or rank or the amount of weight that is lost maydepend on a user's preference. For example, a user may consider all newsitems less than 1 week old to be equally relevant and important whileanother user may consider all news items older than 3 days old lessrelevant or important than news items less than 3 days old. In anotherexample, the weight of content items may decrease more slowly over aperiod of a month for a first user than for a second user. Decay ratesor weight modifiers may be learned automatically or autonomously by thesystem based on user's actions, actions of individuals in the user'ssocial network or external to the user's social network and the like. Inone example, a recommendation system may determine a decay rate based onhow often a content item is accessed over a period of time, a rate ofaccess (e.g., number of accesses per hour, per day, per minute, etc.), arate at which accesses of the content item declines and the like.

Alternatively or additionally, content items whose weights have beenmodified may be displayed in different colors or with a particularindicator to specify that the content item has been modified based onrecency or non-recency. FIG. 8 illustrates an interface 800 providing alist of recommended content items. The list 801 of content items mayinclude a title or name 803 of the item and a category 805 to which thecontent item is associated. List 801 may further include an indicatorcolumn 807 that may display various indicators that specify attributesof the corresponding item. In one example, indicator 809 may specify theweights of items 811 were modified for recency (or lack thereof). Inanother example, indicator 813 may specify that item 815 is aparticularly popular item (e.g., as measured by number of individuals inthe user's social networks that accessed the item). Interface 800 mayfurther include a reset option 817 that allows a user to reset an item'srecency timer. That is, a user may select one or more of items 811 andsubsequently reset the age of the item (i.e., age becomes 0 days old) byselecting option 817. In one or more configurations, a user may enter aspecific age to which the item should be reset. Interface 800 mayfurther include a view option 819, a details option 821 for viewing amore detailed description of a content item and an exit option 823. Viewoption 819 may be used to access the content item (e.g., to startplayback or tune to a corresponding channel).

The recommendation methods, systems and apparatuses described herein maybe used in a media distribution network such as a cable or satellitetelevision system. For example, a user's set-top box may be configuredto send user preferences and user interaction information to a headend(e.g., headend 102 of FIG. 1). Subsequently, the service provider maygenerate recommendations based on the user preferences, userinteractions, interactions of others within social networks,interactions of individuals on a global scale or any combination orsubcombination thereof to generate a list of recommended content items.Additionally, the list of recommended content items may be sorted orranked using similar information. A cable or satellite television systemmay further offer content related options such as chat rooms, additionalcontent information screens, interactive games or functions associatedwith the content and the like.

The methods and features recited herein may further be implementedthrough any number of computer readable media that are able to storecomputer readable instructions. Examples of computer readable media thatmay be used include RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, DVD or other optical disk storage, magneticcassettes, magnetic tape, magnetic storage and the like.

Additionally or alternatively, in at least some embodiments, the methodsand features recited herein may be implemented through one or moreintegrated circuits (ICs). An integrated circuit may, for example, be amicroprocessor that accesses programming instructions or other datastored in a read only memory (ROM). In some such embodiments, the ROMstores programming instructions that cause the IC to perform operationsaccording to one or more of the methods described herein. In at leastsome other embodiments, one or more the methods described herein arehardwired into an IC. In other words, the IC is in such cases anapplication specific integrated circuit (ASIC) having gates and otherlogic dedicated to the calculations and other operations describedherein. In still other embodiments, the IC may perform some operationsbased on execution of programming instructions read from ROM or RAM,with other operations hardwired into gates and other logic of IC.Further, the IC may output image data to a display buffer.

Although specific examples have been described, those skilled in the artwill appreciate that there are numerous variations and permutations ofthe above-described systems and methods that are contained within thespirit and scope of the disclosure as set forth in the appended claims.Additionally, numerous other embodiments, modifications and variationswithin the scope and spirit of the appended claims will occur to personsof ordinary skill in the art from a review of this disclosure.

We claim:
 1. A computer-implemented method for making contentrecommendations comprising: identifying, by a computing device, a socialnetwork of which a first user is a member; determining, by the computingdevice, a first content item accessed by a second user, the second useralso being a member of the social network; determining, by the computingdevice, a social network interest weight for the first content item by:determining a user weight of the second user, the user weightrepresenting an importance of at least one of: the second user and arelationship with the second user within the social network; anddetermining a normalized user weight for the second user by normalizingthe user weight across multiple different social networks; and ordering,by the computing device, a content recommendation list including thedetermined first content item based on the social network interestweight.
 2. A computer-implemented method for making contentrecommendations comprising: determining, by a computing device, multiplecontent items to recommend to a user; ranking, by the computing device,the multiple content items based on a respective ranking value of eachof the multiple content items, wherein the respective ranking value foreach of the multiple content items is determined by: determining a decayrate corresponding to at least one of: a genre of the content item, theuser, and a source of the content item; and applying the decay rate tothe ranking value; and generating a list of the multiple content itemsaccording to the ranking values of the multiple content items.
 3. Acomputer-implemented method for making content recommendationscomprising: generating a ranked list of recommended content items for auser; generating a display including the ranked list of recommendedcontent items, wherein the display includes: genre information for eachof the recommended content items in the ranked list; and a view optionconfigured to request one of the recommended content items.
 4. Thecomputer-implemented method of claim 3, wherein generating the displayincludes providing a recency control option in the display, the recencycontrol option configured to modify a recency value for one or more ofthe content items, wherein the ranked list of recommended content itemsis at least partially ranked based on the recency value.
 5. Thecomputer-implemented method of claim 4, wherein generating the displayfurther includes adding an indicator for a first recommended contentitem, the indicator specifying that a ranking weight of the firstrecommended content item has been modified by a corresponding recencyvalue.